BEaTS-α an open access 3D printed device for in vitro electromechanical stimulation of human induced pluripotent stem cells
Bibliographic record
Abstract
3D printing was used to develop an open access device capable of simultaneous electrical and mechanical stimulation of human induced pluripotent stem cells in 6-well plates. The device was designed using Computer-Aided Design (CAD) and 3D printed with autoclavable, FDA-approved materials. The compact design of the device and materials selection allows for its use inside cell incubators working at high humidity without the risk of overheating or corrosion. Mechanical stimulation of cells was carried out through the cyclic deflection of flexible, translucent silicone membranes by means of a vacuum-controlled, open-access device. A rhythmic stimulation cycle was programmed to create a more physiologically relevant in vitro model. This mechanical stimulation was coupled and synchronized with in situ electrical stimuli. We assessed the capabilities of our device to support cardiac myocytes derived from human induced pluripotent stem cells, confirming that cells cultured under electromechanical stimulation presented a defined/mature cardiomyocyte phenotype. This 3D printed device provides a unique high-throughput in vitro system that combines both mechanical and electrical stimulation, and as such, we foresee it finding applications in the study of any electrically responsive tissue such as muscles and nerves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".